launchSashimiApp: Launch Sashimi R-shiny application

In jmw86069/jambio: Analysis and Visualization of Gene Splice Variants and Transcriptome Data

Launch Sashimi R-shiny application

Description

Launch Sashimi R-shiny application

Usage

launchSashimiApp(
  ...,
  envir = globalenv(),
  options = list(width = 1200),
  verbose = TRUE
)

Arguments

...	additional arguments are passed to sashimiAppConstants().
envir	environment that contains data needed for sashimi plots. If envir=NULL by default it will use globalenv(). Otherwise, call sashimiDataConstants() or sashimiAppConstants() to prepare data inside a specific environment that can be used by this function.
options	list of R-shiny app options, for example two common options are: host to indicate the host or IP address the R-shiny app will bind to respond to requests; and port for the port number. For example: launchSashimiApp(options=list(host="0.0.0.0", port=8080)) where port="0.0.0.0" will listen to any request sent to the current machine, whether by host name, or any valid IP address; and port=8080 will only listen to port 8080.

Details

This function launches the Sashimi visualization R-shiny app.

The R objects required to prepare sashimi plots are defined by the function sashimiAppConstants(), which documents each R object required and how it is used. The sashimiAppConstants() function returns an environment in which the required sashimi plot data is stored and used by the R-shiny app. The default environment is globalenv() however any custom environment can be used, for example with myenv <- new.env().

The most straightforward way to run a new Sashimi R-shiny app is to define filesDF and gtf in the global environment, or define filesDF and gtf inside a custom environment. The required data will be derived from the GTF file gtf. This step is somewhat slow the first time (10 minutes) and saves intermediate files for rapid re-use.

The data derived from the GTF file is listed below. Any data object that already exists in the environment is used in subsequent steps:

• txdb - TranscriptDb from which exonsByGene and exonsByTx are derived.

• tx2geneDF - data.frame with transcript-to-gene relationship, with colnames "gene_name" and "transcript_id". See makeTx2geneFromGtf() for details.

• detectedTx - character vector of detected transcripts, used to match tx2geneDF$transcript_id. When detectedTx is NULL, all entries in tx2geneDF are used. Note that we found it is much better to use only a subset of detected transcripts, mainly because many GTF sources include a large number of potential alternative isoforms, many of which have no supported evidence in any one given cell type. See defineDetectedTx() for one method to define detected transcripts.

• detectedGenes - character vector of genes that match tx2geneDF$gene_name. When detectedGenes is NULL, it is inferred using detectedTx and tx2geneDF$transcript_id.

• exonsByTx, cdsByTx - derived from txdb and annotated to include values from tx2geneDF$gene_name.

• flatExonsByGene, flatExonsByTx - GRangesList objects derived from exonsByGene and exonsByTx, using detectedTx. They also use cdsByTx to indicate coding regions (CDS) of exons.

Note that if detectedTx is not defined, it will use all transcripts at this stage, which can be substantially slower than using only the subset of "observed/detected" transcripts.

An alternative is to supply one detectedGenes gene value, which will prepare only one gene for flatExonsByGene in the R-shiny app. However, the R-shiny app has the option to search all non-detected genes, which are prepared one by one inside the R-shiny app. This process is slightly slower when using the app by a few seconds, and will use all transcripts for detectedTx.

The filesDF object should be a data.frame with at least three colnames:

• "sample_id"

• "type" (with values either "bw" or "junction")

• "url" (a URL or file path to each file.)

If coverage or junctions are available in multiple files, for example sequencing replicates, use the same sample_id for each file, and the coverage and junctions will be combined using the sum, after multiplying an optional "scale_factor" to each file.

For more direct control over the data preparation, including tx2geneDF, detectedTx, exonsByGene, and flatExonsByGene, see sashimiAppConstants() which calls sashimiDataConstants(), both of these functions return an environment that contains the required data.

When the R-shiny app is created, the ui and server components have their environments set to envir - so their context will include the variables defined in that environment.

Value

output from shiny::shinyApp() which is an object of class "shiny.appobj", whose default print method is to run the app.

See Also

Other splicejam R-shiny functions: sashimiAppConstants(), sashimiAppServer(), sashimiAppUI(), sashimiDataConstants()

Examples

# Note: disabled for web page examples
# launchSashimiApp();

jmw86069/jambio documentation built on April 21, 2024, 2:48 p.m.